Image segmentation is critical for quantitative medical imaging and image guided surgical interventions. For instance, in order to provide computer-implemented diagnostic information, quantification of tissue features, such as their thickness, volume, reflectivity, and texture, can be important. In general, the quality of the diagnostic information can be improved by incorporating image recognition functionalities. A particular challenge of image recognition is to identify the boundary layers of the imaged tissues accurately. Identifying the layers in an image is sometimes also referred to as image segmentation.
Beyond diagnostics, another medical area where image segmentation can be very useful is the emerging field of image guided surgical interventions. High quality image segmentation that involves delineating the boundaries of the layered target pathology with high accuracy can improve the outcomes of the surgery substantially. Improved surgical outcomes include lower reoccurrence rates, shorter operation or procedure times, and achieving the surgical goals in a higher percent of the cases.
Layered medical images are typical in ophthalmology, including images of the retina, cornea and the capsule of the nucleus. One of the imaging technologies, the so-called optical coherence tomography, or OCT, demonstrated particularly fast progress in precision, utility and imaging time. OCT is on its way to become one of the most widely used imaging technique in ophthalmology, even approaching the status of the new clinical standard.
Recently, several OCT image segmentation algorithms have been developed. However, these methods are mainly for post processing the images and as such, are not particularly fast. Moreover, these algorithms tend to be limited in their utility. Techniques like “region growing” and “active contour” methods are suitable for segmenting irregular boundaries. However, both require initial seeds to start with and therefore are only semi-automatic. “Support vector machine” and “artificial neural network” methods are computation intensive and require large training data sets. Threshold based approaches are sensitive to intensity variations and require continuous threshold adjustments. Polarization based method rely on a specially designed polarization sensitive hardware system and are therefore not cost effective. Finally, recently proposed graph-based shortest path searches show promise in OCT image segmentation. However, they rely on a complex graph search algorithm that slows down the processing speed, and are thus not suitable for real-time image segmentation. Therefore, there is a need for fast, automated image segmentation algorithms for ophthalmic imaging applications.